Exploring the ecological meanings of temperature sensitivity of ecosystem respiration from different methods

Temperature sensitivity (Q₁₀) of ecosystem respiration (R_e) is a critical parameter for predicting global terrestrial carbon dynamics and its response to climate warming. However, the determination of Q₁₀ has been controversial. In this study, we scrutinized the underpinnings of three mainstream methods to reveal their relationships in estimating Q₁₀ for R_e in the Heihe River Basin, northwest China. Specifically, these methods are Q₁₀ estimated from the long-term method (Q_{10_long}), short-term method (Q_{10_short}), and the low-frequency (Q_{10_lf}) and high-frequency (Q_{10_hf}) signals decomposed by the singular spectrum analysis (SSA) method. We found that: 1) Q_{10_lf} and Q_{10_long} are affected by the confounding effects caused by non-temperature factors, and are 1.8 ± 0.3 and 1.7 ± 0.3, respectively. 2) The high-frequency signals of the SSA method and short-term method have consistent roles in removing the confounding effects. Both Q_{10_short} and Q_{10_hf} reflect the actual response of respiration to temperature. 3) Overall, Q_{10_long} has a larger variability (1.7 ± 0.3) across different biomes, whereas Q_{10_short} and Q_{10_hf} show convergence (1.4 ± 0.2 and 1.3 ± 0.1, respectively). These results highlight the fact that Q₁₀ can be overestimated by the long-term method, whereas the short-term method and high-frequency signals decomposed by the SSA method can obtain closer and convergent values after removing the confounding effects driven by non-temperature factors. Therefore, it is recommended to use the Q₁₀ value estimated by the short-term method or high-frequency signals decomposed by the SSA method to predict carbon dynamics and its response to global warming in Earth system models.